scholarly journals Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System

2021 ◽  
Vol 13 (3) ◽  
pp. 401
Author(s):  
Cadan Cummings ◽  
Yuxin Miao ◽  
Gabriel Dias Paiao ◽  
Shujiang Kang ◽  
Fabián G. Fernández
Keyword(s):  
Machine Learning ◽  
Chlorophyll Content ◽  
Vegetation Index ◽  
Soil Drainage ◽  
Management Information ◽  
Area Index ◽  
Sensing System ◽  
Extreme Gradient Boosting ◽  
Split Plot ◽  
N Status

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

scholarly journals Mapping of the Canopy Openings in Mixed Beech–Fir Forest at Sentinel-2 Subpixel Level Using UAV and Machine Learning Approach

2020 ◽  
Vol 12 (23) ◽  
pp. 3925
Author(s):  
Ivan Pilaš ◽  
Mateo Gašparović ◽  
Alan Novkinić ◽  
Damir Klobučar
Keyword(s):  
Machine Learning ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Predictive Performance ◽  
Spatial Extent ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stochastic Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Sentinel 2

The presented study demonstrates a bi-sensor approach suitable for rapid and precise up-to-date mapping of forest canopy gaps for the larger spatial extent. The approach makes use of Unmanned Aerial Vehicle (UAV) red, green and blue (RGB) images on smaller areas for highly precise forest canopy mask creation. Sentinel-2 was used as a scaling platform for transferring information from the UAV to a wider spatial extent. Various approaches to an improvement in the predictive performance were examined: (I) the highest R2 of the single satellite index was 0.57, (II) the highest R2 using multiple features obtained from the single-date, S-2 image was 0.624, and (III) the highest R2 on the multitemporal set of S-2 images was 0.697. Satellite indices such as Atmospherically Resistant Vegetation Index (ARVI), Infrared Percentage Vegetation Index (IPVI), Normalized Difference Index (NDI45), Pigment-Specific Simple Ratio Index (PSSRa), Modified Chlorophyll Absorption Ratio Index (MCARI), Color Index (CI), Redness Index (RI), and Normalized Difference Turbidity Index (NDTI) were the dominant predictors in most of the Machine Learning (ML) algorithms. The more complex ML algorithms such as the Support Vector Machines (SVM), Random Forest (RF), Stochastic Gradient Boosting (GBM), Extreme Gradient Boosting (XGBoost), and Catboost that provided the best performance on the training set exhibited weaker generalization capabilities. Therefore, a simpler and more robust Elastic Net (ENET) algorithm was chosen for the final map creation.

scholarly journals A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2

2019 ◽  
Vol 11 (5) ◽  
pp. 481 ◽  
Author(s):  
Deepak Upreti ◽  
Wenjiang Huang ◽  
Weiping Kong ◽  
Simone Pascucci ◽  
Stefano Pignatti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Least Squares ◽  
Chlorophyll Content ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Area Index ◽  
Random Forest Tree ◽  
Ground Validation ◽  
Study Sites ◽  
Sentinel 2

This study focuses on the comparison of hybrid methods of estimation of biophysical variables such as leaf area index (LAI), leaf chlorophyll content (LCC), fraction of absorbed photosynthetically active radiation (FAPAR), fraction of vegetation cover (FVC), and canopy chlorophyll content (CCC) from Sentinel-2 satellite data. Different machine learning algorithms were trained with simulated spectra generated by the physically-based radiative transfer model PROSAIL and subsequently applied to Sentinel-2 reflectance spectra. The algorithms were assessed against a standard operational approach, i.e., the European Space Agency (ESA) Sentinel Application Platform (SNAP) toolbox, based on neural networks. Since kernel-based algorithms have a heavy computational cost when trained with large datasets, an active learning (AL) strategy was explored to try to alleviate this issue. Validation was carried out using ground data from two study sites: one in Shunyi (China) and the other in Maccarese (Italy). In general, the performance of the algorithms was consistent for the two study sites, though a different level of accuracy was found between the two sites, possibly due to slightly different ground sampling protocols and the range and variability of the values of the biophysical variables in the two ground datasets. For LAI estimation, the best ground validation results were obtained for both sites using least squares linear regression (LSLR) and partial least squares regression, with the best performances values of R2 of 0.78, rott mean squared error (RMSE) of 0.68 m2 m−2 and a relative RMSE (RRMSE) of 19.48% obtained in the Maccarese site with LSLR. The best results for LCC were obtained using Random Forest Tree Bagger (RFTB) and Bagging Trees (BagT) with the best performances obtained in Maccarese using RFTB (R2 = 0.26, RMSE = 8.88 μg cm−2, RRMSE = 17.43%). Gaussian Process Regression (GPR) was the best algorithm for all variables only in the cross-validation phase, but not in the ground validation, where it ranked as the best only for FVC in Maccarese (R2 = 0.90, RMSE = 0.08, RRMSE = 9.86%). It was found that the AL strategy was more efficient than the random selection of samples for training the GPR algorithm.

scholarly journals Assessing Nitrogen Status in Potted Geranium through Discriminant Analysis of Ground-based Spectral Reflectance Data

HortScience ◽  
2012 ◽  
Vol 47 (3) ◽  
pp. 343-348 ◽  
Author(s):  
Yun-wen Wang ◽  
Bruce L. Dunn ◽  
Daryl B. Arnall
Keyword(s):  
Discriminant Analysis ◽  
Chlorophyll Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rocky Mountain ◽  
Flower Color ◽  
Production Costs ◽  
N Application ◽  
Chlorophyll Meter ◽  
N Status

Nitrogen (N) deficiencies can significantly reduce plant growth as well as flower quantity and quality. However, excessive N application leads to increased production costs and may cause water contamination as a result of runoff. Ground-based remote sensing of plant chlorophyll content offers the possibility to rapidly and inexpensively estimate crop N status. The objective of this study was to test the reliability of three different Normalized Difference Vegetation Index (NDVI) measuring methods and Soil-Plant Analyses Development (SPAD) chlorophyll meter values as indicators of geranium (Pelargonium ×hortorum L.H. Bailey) N status. Two potted geranium cultivars, Rocky Mountain White and Rocky Mountain Dark Red, were supplied with N at 0, 50, 100, and 200 mg·L−1 levels, respectively. NDVI readings were measured at 45 cm above the canopy or media of individual plants or 45 cm above the canopy of a group of plants (four plants treated with the same N rate were placed together). Significant correlations existed between indirect chlorophyll content measurements of SPAD values and NDVI readings regardless of four-pot group or single-pot measurements with N application rates and leaf N concentration. Using a cross-validation technique in discriminant analysis, 70.8% to 79.2% of sample cases were correctly categorized to the corresponding N statuses including very deficient, deficient, and sufficient. Therefore, ground-based, non-destructive measurements of a chlorophyll meter and pocket NDVI unit were able to indicate N status. Considering that flower color can interfere with NDVI measurements, the chlorophyll meter may better determine N content when flowers are present.

scholarly journals A COMPARISON OF MACHINE-LEARNING REGRESSION ALGORITHMS FOR THE ESTIMATION OF LAI USING LANDSAT - 8 SATELLITE DATA

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 679-683
Author(s):  
V. P. Yadav ◽  
R. Prasad ◽  
R. Bala ◽  
A. K. Vishwakarma ◽  
S. A. Yadav ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Vegetation Index ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
Support Vector ◽  
Landsat 8 ◽  
Area Index ◽  
Global Circulation Models

Abstract. The leaf area index (LAI) is one of key variable of crops which plays important role in agriculture, ecology and climate change for global circulation models to compute energy and water fluxes. In the recent research era, the machine-learning algorithms have provided accurate computational approaches for the estimation of crops biophysical parameters using remotely sensed data. The three machine-learning algorithms, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) were used to estimate the LAI for crops in the present study. The three different dates of Landsat-8 satellite images were used during January 2017 – March 2017 at different crops growth conditions in Varanasi district, India. The sampling regions were fully covered by major Rabi season crops like wheat, barley and mustard etc. In total pooled data, 60% samples were taken for the training of the algorithms and rest 40% samples were taken as testing and validation of the machinelearning regressions algorithms. The highest sensitivity of normalized difference vegetation index (NDVI) with LAI was found using RFR algorithms (R2 = 0.884, RMSE = 0.404) as compared to SVR (R2 = 0.847, RMSE = 0.478) and ANNR (R2 = 0.829, RMSE = 0.404). Therefore, RFR algorithms can be used for accurate estimation of LAI for crops using satellite data.

scholarly journals Simulating the Leaf Area Index of Rice from Multispectral Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3663
Author(s):  
Shenzhou Liu ◽  
Wenzhi Zeng ◽  
Lifeng Wu ◽  
Guoqing Lei ◽  
Haorui Chen ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Estimation Accuracy ◽  
Gradient Boosting ◽  
Accurate Estimation ◽  
Area Index ◽  
Red Edge ◽  
Extreme Gradient Boosting

Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

Optimizing hybrid retrieval strategies for crop attribute retrieval using hyperspectral UAV data

2020 ◽  
Author(s):  
Asmaa Abdelbaki ◽  
Martin schlerf ◽  
Jochem Verrelst ◽  
Thomas Udelhoven
Keyword(s):  
Machine Learning ◽  
Radiative Transfer ◽  
Leaf Area Index ◽  
Chlorophyll Content ◽  
Processing Speed ◽  
Vegetation Cover ◽  
Area Index ◽  
Parametric Regression ◽  
Regression Algorithms ◽  
Non Parametric

<p>Unmanned aerial vehicle-based (UAV) hyperspectral imagery is of great significance to estimate crop attributes at a landscape scale, which is required for many environmental and agricultural applications. Multiple methods have been proposed such as empirical regression, radiative transfer, and hybrid models to derive target information (leaf area index (LAI), canopy chlorophyll content (CCC), and fractional vegetation cover (fCover)). Yet, it remains a challenge to select the most suitable method, since each method has its respective advantages and disadvantages. In this study, a hybrid strategy is proposed, as it combines the flexibility of regression with the universality of radiative transfer models (RTM) compared to other retrieval methods concerning model accuracy, computational efficiency under varying sample sizes and different levels of artificial noise. Two datasets of canopy spectra were simulated from two types of Look-up-tables (LUTs) for simulating a range of canopy reflectance-based on a set of input parameters from a Soil-Leaf-canopy RTM. The first type (LUTstd) was derived from a set of independent input parameters, while the other type (LUTreg) relied on the variable correlations by using the Cholesky algorithm. The LUTs were used for training linear and non-linear nonparametric regression algorithms for estimating the relevant parameters for characterizing 27 potato plots. Subsequently, the best approach of non-parametric regression methods was applied to UAV-based hyperspectral data for mapping of crop properties.</p><p>Results showed for LAI and fCover estimates that the principal component regression, partial least square regression, and least squares regression line (PCR, PLSR and LSLR) outperformed any of machine learning regression algorithms (MLRAs) and LUT inversion approaches. Besides, analysis of multiple LUT sizes ranging from 1000 to 17280 revealed that the 1000 simulations were sufficient for training LUTs. Also, adding 1% of noise to the simulations was adequate to imitate the uncertainty of UAV data. By using the independent ground data for validation, the PCR and PLSR methods yielded the lowest errors (R²= 0.81, NRMSE=11.47% for LUTreg than LUTstd (R²= 0.51, NRMSE= 22.61%). Regarding fCover, the accuracy of linear non-parametric and LUT-inversion approaches in LUTreg (R²=0.75 and NRMSE=14.53% for PLSR and R²= 0.78 and NRMSE=14.37% for LUT inversion based) was increased slightly rather than the results obtained from MLRAs (R²= 0.76 and NRMSE= 14.74% for kernel ridge regression (KRR)). Regarding CCC, the best result was obtained using Random forest of tree bagger (RFTB) and fit ensemble (RFFE) for both LUTs. The accuracy of LUTreg did not improve as much as LUTstd through changing sample sizes (R²= 0.80, NRMSE= 14.71% for LUTreg and R²= 0.81, NRMSE= 13.93% for LUTstd). In terms of processing speed, the linear non-parametric methods were the fastest one as compared to MLRAs (PCR=0.0097 and PLSR=0.013 seconds). In conclusion, compared to the two analyzed hybrid strategies (Linear and non-linear non-parametric regression), the use of LUT-inversion is not recommended for large images because of low prediction accuracy and slow processing speed.</p><p><strong>Keywords:</strong> SLC model, LUT inversion based, linear non-parametric regression, machine learning, hybrid model, Leaf area index, fractional vegetation cover, leaf and canopy chlorophyll content.</p>

Evaluation of the chlorophyll meter and GreenSeeker for the assessment of rice nitrogen status

2017 ◽  
Vol 8 (2) ◽  
pp. 359-363 ◽  
Author(s):  
Ke Zhang ◽  
Xiaokang Ge ◽  
Xia Liu ◽  
Zeyu Zhang ◽  
Yan Liang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stem Elongation ◽  
Leaf Nitrogen ◽  
Nitrogen Accumulation ◽  
Plant Analysis ◽  
Area Index ◽  
Spad Readings ◽  
N Status

This work was to evaluate the differences of soil and plant analysis development (SPAD) and normalized difference vegetation index (NDVI) readings and their relationship with leaf nitrogen accumulation (LNA). The study explored new indices to diagnose nitrogen (N) status. These indices were obtained by multiplying SPAD readings and leaf area index (LAI). Linear regression relationships between Chlorophyll values and N indicators showed the SPAD readings (Chl: LNA=0.0546×Chl-0.479, R2=0.94***, P<0.001). The projected results suggested that Chl values could play an important role for improving N status diagnosis from stem elongation to heading stages in paddy rice.

scholarly journals Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

2021 ◽  
Vol 14 (1) ◽  
pp. 120
Author(s):  
Razieh Barzin ◽  
Hossein Lotfi ◽  
Jac J. Varco ◽  
Ganesh C. Bora
Keyword(s):  
Machine Learning ◽  
Grain Yield ◽  
Vegetation Index ◽  
Growth Stages ◽  
Fertilizer N ◽  
Red Edge ◽  
Leaf N Concentration ◽  
Different Growth Stages ◽  
N Status ◽  
Leaf N

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1-V2 stage) and at the V6-V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs.

scholarly journals Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation

2021 ◽  
Vol 13 (16) ◽  
pp. 3281
Author(s):  
Husam A. H. Al-Najjar ◽  
Biswajeet Pradhan ◽  
Bahareh Kalantar ◽  
Maher Ibrahim Sameen ◽  
M. Santosh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Input Data ◽  
Vegetation Index ◽  
Slope Aspect ◽  
Gradient Boosting ◽  
Vegetation Density ◽  
Power Functions ◽  
Susceptibility Modeling ◽  
Extreme Gradient Boosting

Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement).

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